Revisiting the determinants of local government performance
Bibliographic record
Abstract
This present study intends to analyze the factors affecting the performance of the local government of North Sulawesi. The population of this study includes the members of the Regional People’s Representative Assembly and the leaders of Regional Apparatus Organizations managing the Regional Revenue and Expenditure Budget (APBD). Those Regional Apparatus Organizations include Education and Culture Office, Health Office, Regional Revenue Service, Tourism Office, Public Works Office, and Auditor Inspectorate of North Sulawesi Province. The sampling technique used is the saturated sampling technique. Data collection techniques employed consist of an interview, documentation study, and questionnaire dissemination. The analysis is conducted using Partial Least Square (PLS). Based on the analysis, we found that public participation has a significant effect on the transparency of financial management; the internal control system has a significant effect on local government performance; the internal control system has a significant effect on public accountability; the internal control system has a significant effect on the quality of financial reports, and public accountability has a significant effect on the performance of local governments. However, public participation has no significant effect on local government performance; public participation has no significant effect on public accountability; public participation has no significant effect on the quality of financial reports; the internal control system has no significant effect on financial management transparency; financial management transparency has no significant effect on local government performance, and the quality of financial reports has no significant effect on local government performance. This study also reveals that public accountability is the mediating variable between the internal control system and local government performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".